Identifying hidden spatial patterns that define sub-regions characterized by a similar behaviour is a central topic in statistical climatology. This task, often called regionalization, is helpful for recognizing areas in which the variables under consideration have a similar stochastic distribution and thus, potentially, for reducing the dimensionality of the data. Many examples for regionalization are available, spanning from hydrology to weather and climate science. However, the majority of regionalization techniques focuses on the spatial clustering of a single variable of interest and is often not tailored to extremes. Extreme events often have severe impacts, which can be amplified when co-occurring with extremes in other variables. Given the importance of characterizing compound extreme events at the regional scale, here we develop an algorithm that identifies homogeneous spatial sub-regions that are characterized by a common bivariate dependence structure in the tails of a bivariate distribution. In particular, we use a novel non-parametric divergence able to capture the similarities and differences in the tail behaviour of bivariate distributions as the core of our clustering procedure. We apply the approach to identify homogeneous regions that exhibit similar likelihood of compound precipitation and wind extremes in Great Britain and Ireland.

识别定义以相似行为为特征的子区域的隐藏空间模式是统计气候学的中心课题。此任务通常称为区域化，有助于识别其中所考虑的变量具有相似的随机分布的区域，从而有可能降低数据的维数。从水文学到天气和气候科学，可以找到许多区域化的例子。但是，大多数区域化技术都集中在单个关注变量的空间聚类上，并且通常不适合极端情况。极端事件通常会产生严重影响，当与其他变量的极端事件同时发生时，这种影响可能会加剧。鉴于在区域范围内表征复合极端事件的重要性，在这里，我们开发了一种算法，该算法可识别均质空间子区域，这些子区域的特征是在双变量分布的尾部具有共同的双变量依赖结构。特别是，我们使用一种新颖的非参数散度，能够捕获双变量分布的尾部行为的相似性和差异，这是我们聚类过程的核心。我们采用该方法来识别在英国和爱尔兰表现出相似的复合降水和极端风的可能性的均质区域。我们使用一种新颖的非参数散度，能够捕获双变量分布的尾部行为的相似性和差异，这是我们聚类过程的核心。我们采用该方法来识别在英国和爱尔兰表现出相似的复合降水和极端风的可能性的均质区域。我们使用一种新颖的非参数散度，能够捕获双变量分布的尾部行为的相似性和差异，这是我们聚类过程的核心。我们采用该方法来识别在英国和爱尔兰表现出相似的复合降水和极端风的可能性的均质区域。